Abstract

In this paper we present an extension to the KinectFusion algorithm that
permits dense mesh-based mapping of extended scale environments in
real-time. This is achieved through (i) altering the original algorithm
such that the region of space being mapped by the KinectFusion algorithm
can vary dynamically, (ii) extracting a dense point cloud from the regions
that leave the KinectFusion volume due to this variation, and, (iii)
incrementally adding the resulting points to a triangular mesh
representation of the environment. The system is implemented as a set of
hierarchical multi-threaded components which are capable of operating in
real-time. The architecture facilitates the creation and integration of
new modules with minimal impact on the performance on the dense volume
tracking and surface reconstruction modules. We provide experimental
results demonstrating the system's ability to map areas considerably
beyond the scale of the original KinectFusion algorithm including a two
story apartment and an extended sequence taken from a car at night. In
order to overcome failure of the iterative closest point (ICP) based
odometry in areas of low geometric features we have evaluated the Fast
Odometry from Vision (FOVIS) system as an alternative. We provide a
comparison between the two approaches where we show a trade off between
the reduced drift of the visual odometry approach and the higher local
mesh quality of the ICP-based approach. Finally we present ongoing work on
incorporating full simultaneous localisation and mapping (SLAM) pose-graph
optimisation.